Amazon SAA-C03 Practice Test - Questions Answers, Page 74

Understanding the Requirement: The company needs to process images as they are uploaded to S3 in a cost-effective manner, currently using an EC2 Spot Fleet for nightly processing.

Analysis of Options:

S3 Event Notifications to SQS and Lambda: This setup allows for event-driven processing with Lambda, which scales automatically based on the number of messages in the queue. It is cost-effective as Lambda charges are based on the compute time used.

S3 Event Notifications to SQS and EC2 Reserved Instance: Involves managing EC2 instances, which adds operational overhead and is less cost-effective.

S3 Event Notifications to SNS and ECS: More complex and potentially less cost-effective compared to using Lambda for simple processing tasks.

S3 Event Notifications to SNS: Requires additional configuration and management to process messages.

Best Solution:

S3 Event Notifications to SQS and Lambda: This option is the most cost-effective and scalable, leveraging AWS managed services with minimal operational overhead.

Amazon S3 Event Notifications

Amazon SQS

AWS Lambda

Requirement Analysis: Need secure, direct connectivity from an on-premises client to an RDS cluster, accessible only when in the office.

VPC with Private Subnets: Ensures the RDS cluster is not publicly accessible, enhancing security.

Site-to-Site VPN: Provides secure, encrypted connection between on-premises office and AWS VPC.

Implementation:

Create a VPC with two private subnets.

Launch the RDS cluster in the private subnets.

Set up a Site-to-Site VPN connection with a customer gateway in the office.

Conclusion: This setup ensures secure and direct connectivity with minimal exposure, meeting the requirement for secure access from the office.

Reference

AWS Site-to-Site VPN: AWS Site-to-Site VPN Documentation

Amazon RDS: Amazon RDS Documentation

Amazon Neptune: Neptune is a fully managed graph database service that is optimized for storing and querying highly connected data. It supports both property graph and RDF graph models, making it suitable for applications that need to analyze relationships between data entities.

Neptune Streams: Neptune Streams captures changes to the graph and streams these changes to other AWS services. This is useful for applications that need to monitor and respond to changes in real-time, such as providing recommendations based on user interactions and relationships.

Least Operational Overhead: Using Neptune Streams directly with Amazon Neptune ensures that the solution is tightly integrated, reducing the need for additional components and minimizing operational overhead. This integration simplifies the architecture by eliminating the need for a separate service like Kinesis for change processing.

Amazon Neptune Documentation

Neptune Streams Documentation

Amazon S3 Intelligent-Tiering: This storage class is designed to optimize costs by automatically moving data between two access tiers (frequent and infrequent) when access patterns change. It provides cost savings without performance impact or operational overhead.

S3 Lifecycle Rules: By creating an S3 Lifecycle rule, the company can automatically transition objects to the Intelligent-Tiering storage class. This eliminates the need for manual intervention and ensures that objects are moved to the most cost-effective storage tier based on their access patterns.

Operational Efficiency: Intelligent-Tiering requires no additional management and delivers immediate availability for frequently accessed objects. This makes it the most operationally efficient solution for the given requirements.

Amazon S3 Intelligent-Tiering

S3 Lifecycle Policies

Amazon S3 Standard-IA: This storage class is designed for data that is accessed less frequently but requires rapid access when needed. It offers lower storage costs compared to S3 Standard while still providing high availability and durability.

Access Patterns: Since the files are frequently accessed in the first 30 days and rarely accessed afterward, transitioning them to S3 Standard-IA after 30 days aligns with their access patterns and reduces storage costs significantly.

Lifecycle Policy: Implementing a lifecycle policy to transition the files to S3 Standard-IA ensures automatic management of the data lifecycle, moving files to a lower-cost storage class without manual intervention. Deleting the files after 4 years further optimizes costs by removing data that is no longer needed.

Amazon S3 Storage Classes

S3 Lifecycle Configuration

Gateway Endpoint for Amazon S3: A VPC endpoint for Amazon S3 allows you to privately connect your VPC to Amazon S3 without requiring an internet gateway, NAT device, VPN connection, or AWS Direct Connect connection.

Provisioning the Endpoint:

Navigate to the VPC Dashboard.

Select 'Endpoints' and create a new endpoint.

Choose the service name for S3 (com.amazonaws.region.s3).

Select the appropriate VPC and subnets.

Adjust the route tables of the subnets to include the new endpoint.

Update Route Tables: Modify the route tables of the subnets to direct traffic destined for S3 to the newly created endpoint. This ensures that traffic to S3 does not go through the NAT instance, avoiding the saturated network and eliminating timeouts.

Operational Efficiency: This solution minimizes operational overhead by removing dependency on the NAT instance and avoiding internet traffic, leading to more stable and secure S3 interactions.

VPC Endpoints for Amazon S3

Creating a Gateway Endpoint

Understanding the Requirement: The application running on EC2 instances in private subnets needs frequent access to large confidential files stored in S3, minimizing data transfer costs.

Analysis of Options:

Gateway Endpoint for S3: Provides a secure, scalable, and cost-effective way for instances in private subnets to access S3 without using the internet or NAT gateways, thus minimizing data transfer costs.

Single NAT Gateway: Incurs additional costs for data transfer through the NAT gateway, which is not cost-effective.

PrivateLink Interface Endpoint for S3: Primarily used for accessing AWS services over a private connection but is more complex and costly compared to a gateway endpoint for S3.

Multiple NAT Gateways: Increases costs significantly and adds complexity without offering the cost benefits of a gateway endpoint.

Best Solution:

Gateway Endpoint for S3: This solution provides the required access with the least data transfer costs and minimal complexity.

VPC Endpoints for Amazon S3

Gateway Endpoints

Understanding the Requirement: The application experiences slow performance at the start of peak hours, but normalizes after a few hours. The goal is to ensure proper performance at the beginning of peak hours.

Analysis of Options:

Application Load Balancer: Ensures proper traffic distribution but does not address the need to have sufficient instances running at the start of peak hours.

Dynamic Scaling Policy Based on Memory or CPU Utilization: While dynamic scaling reacts to usage metrics, it may not preemptively scale in anticipation of peak hours, leading to delays as new instances are launched and become available.

Scheduled Scaling Policy: This allows the Auto Scaling group to launch instances ahead of time, ensuring that enough instances are available and ready to handle the increased load right at the start of peak hours.

Best Solution:

Scheduled Scaling Policy: This approach ensures that new instances are launched and ready before peak hours begin, addressing the slow performance issue at the start of peak periods.

Scheduled Scaling for Amazon EC2 Auto Scaling

Understanding the Requirement: The company needs to collect transaction data in real time, avoid data duplication, and perform additional processing without managing infrastructure.

Analysis of Options:

SQS FIFO Queue with Lambda: Ensures data is processed in order and prevents duplication. Lambda handles processing without the need to manage servers.

SQS FIFO Queue with AWS Batch: While this ensures no duplicates, it introduces additional complexity and management overhead with AWS Batch.

Kinesis Data Streams with AWS Batch and EC2: Involves more components and infrastructure management, which is against the requirement of not wanting to manage infrastructure.

Step Functions with Lambda and EC2: Involves setting up multiple services and still requires managing EC2 instances, increasing complexity.

Best Solution:

SQS FIFO Queue with Lambda: This combination ensures real-time data processing, prevents duplication, and minimizes infrastructure management, meeting all requirements efficiently.

Amazon SQS FIFO Queues

AWS Lambda and SQS Integration